library(tidyverse)
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library(readxl)
library(rvest)
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library(httr)
library(lubridate)
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library(flexdashboard)
library(plotly)
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Import dataset

raw_sub_crime = 
  read_csv("./data/subwaycrime.csv") %>% 
  janitor::clean_names()
## New names:
## * `` -> ...1
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 6244 Columns: 37
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## Delimiter: ","
## chr  (20): BORO_NM, CMPLNT_FR_DT, CMPLNT_TO_DT, CRM_ATPT_CPTD_CD, JURIS_DESC...
## dbl  (11): ...1, CMPLNT_NUM, ADDR_PCT_CD, JURISDICTION_CODE, KY_CD, PD_CD, T...
## lgl   (4): HADEVELOPT, HOUSING_PSA, LOC_OF_OCCUR_DESC, PARKS_NM
## time  (2): CMPLNT_FR_TM, CMPLNT_TO_TM
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
raw_sub_station = 
  read_xlsx("./data/subway_info_final.xlsx") %>% 
  janitor::clean_names()

Crime event v.s. time

sub_crime = 
  raw_sub_crime %>% 
  select(cmplnt_fr_dt, cmplnt_fr_tm, ofns_desc, station_name, latitude, longitude) %>% 
  rename("date" = "cmplnt_fr_dt", "time" = "cmplnt_fr_tm", "crime_event" = "ofns_desc") %>% 
  drop_na(time) %>% 
  mutate(
    time = case_when(
      hms("00:00:00") <= time & time < hms("02:00:00") ~hms("00:00:00"),
      hms("02:00:00") <= time & time < hms("04:00:00") ~hms("04:00:00"),
      hms("04:00:00") <= time & time < hms("06:00:00") ~hms("04:00:00"),
      hms("06:00:00") <= time & time < hms("08:00:00") ~hms("08:00:00"),
      hms("08:00:00") <= time & time < hms("10:00:00") ~hms("08:00:00"),
      hms("10:00:00") <= time & time < hms("12:00:00") ~hms("12:00:00"),
      hms("12:00:00") <= time & time < hms("14:00:00") ~hms("12:00:00"),
      hms("14:00:00") <= time & time < hms("16:00:00") ~hms("16:00:00"),
      hms("16:00:00") <= time & time < hms("18:00:00") ~hms("16:00:00"),
      hms("18:00:00") <= time & time < hms("20:00:00") ~hms("20:00:00"),
      hms("20:00:00") <= time & time < hms("23:59:59") ~hms("20:00:00"),
    )
  ) %>% 
  mutate(time = as.character(time)) %>% 
  mutate(date = substring(as.character(as.Date(date, "%m/%d/%y")),1,7)) %>% 
  filter(crime_event %in% c("CRIMINAL MISCHIEF & RELATED OF", "ASSAULT 3 & RELATED OFFENSES","HARRASSMENT 2","GRAND LARCENY","DANGEROUS DRUGS","FELONY ASSAULT","ROBBERY","PETIT LARCENY","FORGERY","SEX CRIMES","OFF. AGNST PUB ORD SENSBLTY &","DANGEROUS WEAPONS","THEFT OF SERVICES","OFFENSES AGAINST PUBLIC ADMINI"))

Crime events over time

plot_2 = 
  sub_crime %>% 
  group_by(date) %>% 
  summarise(event_num = n()) %>% 
  plot_ly(
    x = ~date, y = ~event_num, type = "scatter", mode = "markers"
  )

layout(plot_2, title = "Crime events over time", xaxis = list(title = "Month"), yaxis = list(title = "Number of Crime Events"))

Crime events number by time

bar_plot = 
  sub_crime %>% 
  mutate(time = as.factor(time)) %>% 
  ggplot(aes(x = time %>% fct_infreq(), fill = crime_event)) + 
  geom_histogram(stat = "count", width = 0.9, height = 2) + 
  labs(
    title = "Frequency of crime events v.s. Time points", 
    x = "Occurrence time", 
    y = "Frequency of crime events") + 
  theme_bw() + 
  theme(
    plot.title = element_text(hjust = 1), 
    legend.position = "bottom",
    legend.text = element_text(size = 8)) + 
  guides(col = guide_legend(nrow = 2))
## Warning: Ignoring unknown parameters: binwidth, bins, pad, height
ggplotly(bar_plot) %>%
  layout(legend = list(
      orientation = "h",
      xanchor = "center",
      yanchor = "top",
      x = 0.3,
      y = - 0.3
    )
  )

Response time

crime_response_time = 
  raw_sub_crime %>% 
  rename("start_date" = "cmplnt_fr_dt", "start_time" = "cmplnt_fr_tm", "end_date" = "cmplnt_to_dt", "end_time" = "cmplnt_to_tm", "crime_event" = "ofns_desc") %>% 
  drop_na(start_time, end_time) %>%
  filter(crime_event %in% c("CRIMINAL MISCHIEF & RELATED OF", "ASSAULT 3 & RELATED OFFENSES","HARRASSMENT 2","GRAND LARCENY","DANGEROUS DRUGS","FELONY ASSAULT","ROBBERY","PETIT LARCENY","FORGERY","SEX CRIMES","OFF. AGNST PUB ORD SENSBLTY &","DANGEROUS WEAPONS","THEFT OF SERVICES","OFFENSES AGAINST PUBLIC ADMINI")) %>%
  mutate(start_date = as.character(as.Date(start_date, "%m/%d/%y")), 
         end_date = as.character(as.Date(end_date, "%m/%d/%y"))) %>% 
  mutate(start = as.POSIXct(paste(start_date, start_time), format = "%Y-%m-%d %H:%M:%S"), 
         end = as.POSIXct(paste(end_date, end_time), format = "%Y-%m-%d %H:%M:%S")) %>% 
  mutate(response_time = as.numeric(difftime(end, start, units = "mins"))) %>% 
  mutate(
    event_time = as.character(case_when(
      hms("00:00:00") <= start_time & start_time < hms("02:00:00") ~hms("00:00:00"),
      hms("02:00:00") <= start_time & start_time < hms("04:00:00") ~hms("04:00:00"),
      hms("04:00:00") <= start_time & start_time < hms("06:00:00") ~hms("04:00:00"),
      hms("06:00:00") <= start_time & start_time < hms("08:00:00") ~hms("08:00:00"),
      hms("08:00:00") <= start_time & start_time < hms("10:00:00") ~hms("08:00:00"),
      hms("10:00:00") <= start_time & start_time < hms("12:00:00") ~hms("12:00:00"),
      hms("12:00:00") <= start_time & start_time < hms("14:00:00") ~hms("12:00:00"),
      hms("14:00:00") <= start_time & start_time < hms("16:00:00") ~hms("16:00:00"),
      hms("16:00:00") <= start_time & start_time < hms("18:00:00") ~hms("16:00:00"),
      hms("18:00:00") <= start_time & start_time < hms("20:00:00") ~hms("20:00:00"),
      hms("20:00:00") <= start_time & start_time < hms("23:59:59") ~hms("20:00:00"),
    ))
  )

crime_response_time %>% 
  ggplot(aes(x = event_time, y = response_time)) + geom_boxplot()
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).